7 Enhancing Driver Visual Guidance Through Mobility Digital Twin

With the rapid development of intelligent vehicles and advanced driver-assistance systems (ADAS), a new trend is that intelligent vehicles with different levels of automation will coexist in large-scale traffic scenarios. In such scenarios, the automation level of intelligent vehicles could range from the Society of Automotive Engineers level 0 (i.e., no automation) to level 5 (i.e., full automation) and could involve different degrees of human driver engagements. Therefore, necessary visual guidance for drivers is vitally important under this situation to prevent potential risks. Although common environment perception systems have achieved great success, they usually heavily rely on onboard storage and computing, limiting their functionalities by multiple constraints, such as computing power, accessibility to big data, and easiness of deployments and modifications. In this chapter, a mobility digital twin (MDT) framework is introduced, which is defined as an artificial intelligence (AI)-based data-driven device-edge-cloud framework for mobility services. By taking an advantage from vehicle connectivity, the MDT framework enables more powerful, shareable, manageable, and extendable mobility services. A case study leveraging the MDT framework to providing better visual guidance will be presented. Future challenges of the MDT framework are discussed toward the end of the chapter, including standardization, AI for computing, public or private cloud service, and network heterogeneity.